Part 1 Advances in remote sensing technologies 1.Advances in remote/aerial sensing of crop water status: Wenxuan Guo, Texas Tech University and Texas A&M AgriLife Research, USA; and Haibin Gu, Bishnu Ghimire and Oluwatola Adedeji, Texas Tech University, USA; 2.Advances in remote sensing technologies for assessing crop health: Michael Schirrmann, Leibniz Institute for Agricultural Engineering and Bioeconomy, Germany; 3.Advances in remote/aerial sensing techniques for monitoring soil health: Jeffrey P. Walker and Nan Ye, Monash University, Australia; and Liujun Zhu, Monash University, Australia and Yangtze Institute for Conservation and Development, Hohai University, China; Part 2 Advances in proximal sensing technologies 4.Advances in using proximal spectroscopic sensors to assess soil health: Kenneth A. Sudduth and Kristen S. Veum, USDA-ARS, USA; 5.

Advances in using proximal ground penetrating radar sensors to assess soil health: Katherine Grote, Missouri University of Science and Technology, USA; 6.Using proximal electromagnetic/electrical resistivity/electrical sensors to assess soil health: Alain Tabbagh,Sorbonne Université, EPHE, UMR7619, Métis,4 place Jussieu 75252 Paris CEDEX 05, France; and Seger Maud and Cousin Isabelle, INRAE, Centre Val de Loire, UR0272 SOLS, 2163 Avenue de la Pomme de Pin, CS40001 Ardon, F-45075 Orléans Cedex 2, France; 7.Using ground-penetrating radar to map agricultural subsurface drainage systems for economic and environmental benefit: Barry Allred, USDA-ARS - Soil Drainage Research Unit, USA; and Triven Koganti, Aarhus University, Denmark; Part 3 Advances in sensor data analytics 8.Advances in machine vision technologies for the measurement of soil texture, structure and topography: Jean-Marc Gilliot, AgroParisTech Paris Saclay University, France; and Ophélie Sauzet, University of Applied Sciences of Western Switzerland, The Geneva Institute of Technology, Architecture and Landscape (HEPIA), Soils and Substrates Group, Institute Land-Nature-Environment (inTNE Institute), Switzerland; 9.Using machine learning to identify and diagnose crop disease: Megan Long, John Innes Centre, UK; 10.Advances in proximal sensor fusion and multi-sensor platforms for improved crop management: David W. Franzen and Anne M. Denton, North Dakota State University, USA; 11.

Using remote and proximal sensor data in precision agriculture applications: Luciano S. Shiratsuchi and Franciele M. Carneiro, Louisiana State University, USA; Francielle M. Ferreira, São Paulo State University (UNESP), Brazil; Phillip Lanza and Fagner A. Rontani, Louisiana State University, USA; Armando L. Brito Filho, São Paulo State University (UNESP), Brazil; Getúlio F. Seben Junior, State University of Mato Grosso (UNEMAT), Brazil; Ziany N. Brandao, Brazilian Agricultural Research Corporation (EMBRAPA), Brazil; Carlos A.

Silva Junior, State University of Mato Grosso (UNEMAT), Brazil; Paulo E. Teodoro, Federal University of Mato Grosso do Sul (UFMS), Brazil; and Syam Dodla, Louisiana State University, USA;.